87 lines
2.7 KiB
Python
87 lines
2.7 KiB
Python
#!/bin/python
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import numpy as np
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from scipy.ndimage.filters import convolve, gaussian_filter
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from scipy.misc import imread, imshow
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def CannyEdgeDetector(im, blur = 1, highThreshold = 91, lowThreshold = 31):
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im = np.array(im, dtype=float) #Convert to float to prevent clipping values
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#Gaussian blur to reduce noise
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im2 = gaussian_filter(im, blur)
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#Use sobel filters to get horizontal and vertical gradients
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im3h = convolve(im2,[[-1,0,1],[-2,0,2],[-1,0,1]])
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im3v = convolve(im2,[[1,2,1],[0,0,0],[-1,-2,-1]])
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#Get gradient and direction
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grad = np.power(np.power(im3h, 2.0) + np.power(im3v, 2.0), 0.5)
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theta = np.arctan2(im3v, im3h)
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thetaQ = (np.round(theta * (5.0 / np.pi)) + 5) % 5 #Quantize direction
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#Non-maximum suppression
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gradSup = grad.copy()
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for r in range(im.shape[0]):
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for c in range(im.shape[1]):
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#Suppress pixels at the image edge
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if r == 0 or r == im.shape[0]-1 or c == 0 or c == im.shape[1] - 1:
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gradSup[r, c] = 0
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continue
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tq = thetaQ[r, c] % 4
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if tq == 0: #0 is E-W (horizontal)
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if grad[r, c] <= grad[r, c-1] or grad[r, c] <= grad[r, c+1]:
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gradSup[r, c] = 0
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if tq == 1: #1 is NE-SW
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if grad[r, c] <= grad[r-1, c+1] or grad[r, c] <= grad[r+1, c-1]:
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gradSup[r, c] = 0
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if tq == 2: #2 is N-S (vertical)
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if grad[r, c] <= grad[r-1, c] or grad[r, c] <= grad[r+1, c]:
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gradSup[r, c] = 0
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if tq == 3: #3 is NW-SE
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if grad[r, c] <= grad[r-1, c-1] or grad[r, c] <= grad[r+1, c+1]:
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gradSup[r, c] = 0
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#Double threshold
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strongEdges = (gradSup > highThreshold)
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#Strong has value 2, weak has value 1
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thresholdedEdges = np.array(strongEdges, dtype=np.uint8) + (gradSup > lowThreshold)
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#Tracing edges with hysteresis
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#Find weak edge pixels near strong edge pixels
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finalEdges = strongEdges.copy()
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currentPixels = []
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for r in range(1, im.shape[0]-1):
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for c in range(1, im.shape[1]-1):
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if thresholdedEdges[r, c] != 1:
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continue #Not a weak pixel
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#Get 3x3 patch
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localPatch = thresholdedEdges[r-1:r+2,c-1:c+2]
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patchMax = localPatch.max()
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if patchMax == 2:
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currentPixels.append((r, c))
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finalEdges[r, c] = 1
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#Extend strong edges based on current pixels
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while len(currentPixels) > 0:
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newPix = []
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for r, c in currentPixels:
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for dr in range(-1, 2):
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for dc in range(-1, 2):
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if dr == 0 and dc == 0: continue
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r2 = r+dr
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c2 = c+dc
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if thresholdedEdges[r2, c2] == 1 and finalEdges[r2, c2] == 0:
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#Copy this weak pixel to final result
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newPix.append((r2, c2))
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finalEdges[r2, c2] = 1
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currentPixels = newPix
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return finalEdges
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if __name__=="__main__":
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im = imread("test.jpg", mode="L") #Open image, convert to greyscale
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finalEdges = CannyEdgeDetector(im)
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imshow(finalEdges)
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